We have implemented our framework using the deep learning library PyTorch [1]} and have optimized all networks together using the Adam optimizer [2]}. Furthermore, to train implicit networks, we map the input data to \([-1,1]\)  as recommended in [3]}. Additionally, to avoid over-fitting, we add \(L_2\) -regularization (also referred to as weight decay) of the parameters of the networks and set the regularization parameter to \(10^{-4}\)  for all examples. All the networks are trained for \(15~000\)  epochs with batch size 1, and learning rates used to train networks are stated in each example in their respective subsections. We have run all our experiments on a A100 GPU.
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